Personalized Text to Speech Synthesis through Few Shot Speaker Adaptation with Contrastive Learning
Bibliographic record
Abstract
Personalized text-to-speech (TTS) synthesis has the goal of producing natural and expressive speech that emulates the voice of a target speaker with a minimum of data. The models of traditional neural TTS, including Tacotron 2 and Fast Speech 2, need to be trained in large amounts of speaker-specific data and can thus not easily be personalized quickly. We suggest CL-FS-TTS (Contrastive Learning based Few-Shot Text-to-Speech) to solve this problem, a new framework that uses contrastive speaker representation learning to adapt the speaker using only 1030 seconds of reference audio. The CL-FS-TTS architecture has two encoders: a content encoder that identifies linguistic features of text and a speaker encoder trained with the help of supervised contrastive learning to maximize speaker dissimilarity. In adaptation, the model matches speaker embeddings with generated mel-spectrograms with a contrastive consistency loss, enhancing voice and prosodic consistency. We compare CL-FS-TTS with Tacotron 2, Fast Speech 2, AdaSpeech, YourTTS, and Meta-TTS in terms of Mean Opinion Score (MOS), Speaker Similarity Score (SSS), Mel Cepstral Distortion (MCD) and Word Error Rate (WER). The experimental outcomes indicate that CL-FS-TTS has a higher naturalness and similarity of the speaker besides 40% less adaptation time in comparison with baselines. The suggested model lays the foundation of an efficient and strong model of high-quality personalized TTS synthesis in the situation of data scarcity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".